from __future__ import annotations

import gzip
import io
import json
import math
import re
import zlib
from collections import OrderedDict
from datetime import datetime
from decimal import Decimal as D
from io import BytesIO
from typing import TYPE_CHECKING

import zstandard
from hypothesis import given

from polars.datatypes.group import FLOAT_DTYPES

if TYPE_CHECKING:
    from pathlib import Path

import orjson
import pytest

import polars as pl
from polars.exceptions import ComputeError
from polars.testing import assert_frame_equal, assert_series_equal
from polars.testing.parametric import dataframes


@pytest.mark.may_fail_cloud  # reason: object
def test_write_json_example() -> None:
    df = pl.DataFrame({"a": [1, 2, 3], "b": ["a", "b", None]})
    out = df.write_json()
    assert out == '[{"a":1,"b":"a"},{"a":2,"b":"b"},{"a":3,"b":null}]'

    # Test round trip
    f = io.BytesIO()
    f.write(out.encode())
    f.seek(0)
    result = pl.read_json(f)
    assert_frame_equal(result, df)


@pytest.mark.may_fail_cloud  # reason: object
@given(
    df=dataframes(
        min_size=1,
        allow_infinity=False,
        allow_nan=False,
        excluded_dtypes=[
            pl.Binary,
            pl.Categorical,
            pl.Date,
            pl.Datetime,
            pl.Decimal,
            pl.Duration,  # See #20198
            pl.Enum,
            pl.Extension,
            pl.Int128,
            pl.Struct,
            pl.Time,
            pl.UInt128,
        ],
    )
)
def test_write_json_roundtrip(df: pl.DataFrame) -> None:
    f = io.BytesIO()
    f.write(df.write_json().encode())
    f.seek(0)
    result = pl.read_json(f, schema=df.schema)
    assert_frame_equal(result, df)


@given(
    df=dataframes(
        min_size=1,
        allowed_dtypes=FLOAT_DTYPES,
    )
)
def test_write_json_floats(df: pl.DataFrame) -> None:
    f = io.BytesIO()
    f.write(df.write_json().encode())
    f.seek(0)
    result = pl.read_json(f, schema=df.schema)

    df = df.select(
        pl.all()
        .replace(math.inf, None)
        .replace(-math.inf, None)
        .replace(math.nan, None)
    )
    assert_frame_equal(result, df)


def test_write_json_categoricals() -> None:
    data = {"column": ["test1", "test2", "test3", "test4"]}
    df = pl.DataFrame(data).with_columns(pl.col("column").cast(pl.Categorical))
    expected = (
        '[{"column":"test1"},{"column":"test2"},{"column":"test3"},{"column":"test4"}]'
    )
    assert df.write_json() == expected


def test_write_json_duration() -> None:
    df = pl.DataFrame(
        {
            "a": pl.Series(
                [91762939, 91762890, 6020836], dtype=pl.Duration(time_unit="ms")
            )
        }
    )

    # we don't guarantee a format, just round-circling
    value = df.write_json()
    expected = '[{"a":"PT91762.939S"},{"a":"PT91762.89S"},{"a":"PT6020.836S"}]'
    assert value == expected


def test_write_json_time() -> None:
    ns = 1_000_000_000
    df = pl.DataFrame(
        {
            "a": pl.Series(
                [7291 * ns + 54321, 54321 * ns + 12345, 86399 * ns],
                dtype=pl.Time,
            ),
        }
    )

    value = df.write_json()
    expected = (
        '[{"a":"02:01:31.000054321"},{"a":"15:05:21.000012345"},{"a":"23:59:59"}]'
    )
    assert value == expected


def test_write_json_list_of_arrays() -> None:
    df = pl.DataFrame(
        {
            "a": pl.Series(
                [[(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)], [(7.0, 8.0, 9.0)]],
                dtype=pl.List(pl.Array(pl.Float32, 3)),
            ),
        }
    )

    value = df.write_json()
    expected = '[{"a":[[1.0,2.0,3.0],[4.0,5.0,6.0]]},{"a":[[7.0,8.0,9.0]]}]'
    assert value == expected


def test_write_json_decimal() -> None:
    df = pl.DataFrame({"a": pl.Series([D("1.00"), D("2.00"), None])})

    # we don't guarantee a format, just round-circling
    value = df.write_json()
    assert value == """[{"a":"1.00"},{"a":"2.00"},{"a":null}]"""


def test_json_infer_schema_length_11148() -> None:
    response = [{"col1": 1}] * 2 + [{"col1": 1, "col2": 2}] * 1
    with pytest.raises(
        pl.exceptions.ComputeError, match="extra field in struct data: col2"
    ):
        pl.read_json(json.dumps(response).encode(), infer_schema_length=2)

    response = [{"col1": 1}] * 2 + [{"col1": 1, "col2": 2}] * 1
    result = pl.read_json(json.dumps(response).encode(), infer_schema_length=3)
    assert set(result.columns) == {"col1", "col2"}


def test_to_from_buffer_arraywise_schema() -> None:
    buf = io.StringIO(
        """
    [
        {"a": 5, "b": "foo", "c": null},
        {"a": 11.4, "b": null, "c": true, "d": 8},
        {"a": -25.8, "b": "bar", "c": false}
    ]"""
    )

    read_df = pl.read_json(buf, schema={"b": pl.String, "e": pl.Int16})

    assert_frame_equal(
        read_df,
        pl.DataFrame(
            {
                "b": pl.Series(["foo", None, "bar"], dtype=pl.String),
                "e": pl.Series([None, None, None], dtype=pl.Int16),
            }
        ),
    )


def test_to_from_buffer_arraywise_schema_override() -> None:
    buf = io.StringIO(
        """
    [
        {"a": 5, "b": "foo", "c": null},
        {"a": 11.4, "b": null, "c": true, "d": 8},
        {"a": -25.8, "b": "bar", "c": false}
    ]"""
    )

    read_df = pl.read_json(buf, schema_overrides={"c": pl.Int64, "d": pl.Float64})

    assert_frame_equal(
        read_df,
        pl.DataFrame(
            {
                "a": pl.Series([5, 11.4, -25.8], dtype=pl.Float64),
                "b": pl.Series(["foo", None, "bar"], dtype=pl.String),
                "c": pl.Series([None, 1, 0], dtype=pl.Int64),
                "d": pl.Series([None, 8, None], dtype=pl.Float64),
            }
        ),
        check_column_order=False,
    )


def test_write_ndjson() -> None:
    df = pl.DataFrame({"a": [1, 2, 3], "b": ["a", "b", None]})
    out = df.write_ndjson()
    assert out == '{"a":1,"b":"a"}\n{"a":2,"b":"b"}\n{"a":3,"b":null}\n'

    # Test round trip
    f = io.BytesIO()
    f.write(out.encode())
    f.seek(0)
    result = pl.read_ndjson(f)
    assert_frame_equal(result, df)


def test_write_ndjson_with_trailing_newline() -> None:
    input = """{"Column1":"Value1"}\n"""
    df = pl.read_ndjson(io.StringIO(input))

    expected = pl.DataFrame({"Column1": ["Value1"]})
    assert_frame_equal(df, expected)


def test_read_ndjson_empty_array() -> None:
    assert pl.read_ndjson(io.StringIO("""{"foo": {"bar": []}}""")).to_dict(
        as_series=False
    ) == {"foo": [{"bar": []}]}


def test_ndjson_nested_null() -> None:
    json_payload = """{"foo":{"bar":[{}]}}"""
    df = pl.read_ndjson(io.StringIO(json_payload))

    # 'bar' represents an empty list of structs; check the schema is correct (eg: picks
    # up that it IS a list of structs), but confirm that list is empty (ref: #11301)
    # We don't support empty structs yet. So Null is closest.
    assert df.schema == {"foo": pl.Struct([pl.Field("bar", pl.List(pl.Struct({})))])}
    assert df.to_dict(as_series=False) == {"foo": [{"bar": [{}]}]}


def test_ndjson_nested_string_int() -> None:
    ndjson = """{"Accumulables":[{"Value":32395888},{"Value":"539454"}]}"""
    assert pl.read_ndjson(io.StringIO(ndjson)).to_dict(as_series=False) == {
        "Accumulables": [[{"Value": "32395888"}, {"Value": "539454"}]]
    }


def test_json_supertype_infer() -> None:
    json_string = """[
{"c":[{"b": [], "a": "1"}]},
{"c":[{"b":[]}]},
{"c":[{"b":["1"], "a": "1"}]}]
"""
    python_infer = pl.from_records(json.loads(json_string))
    polars_infer = pl.read_json(io.StringIO(json_string))
    assert_frame_equal(python_infer, polars_infer)


@pytest.mark.may_fail_cloud  # reason: object
def test_ndjson_sliced_list_serialization() -> None:
    data = {"col1": [0, 2], "col2": [[3, 4, 5], [6, 7, 8]]}
    df = pl.DataFrame(data)
    f = io.BytesIO()
    sliced_df = df[1, :]
    sliced_df.write_ndjson(f)
    assert f.getvalue() == b'{"col1":2,"col2":[6,7,8]}\n'


def test_json_deserialize_9687() -> None:
    response = {
        "volume": [0.0, 0.0, 0.0],
        "open": [1263.0, 1263.0, 1263.0],
        "close": [1263.0, 1263.0, 1263.0],
        "high": [1263.0, 1263.0, 1263.0],
        "low": [1263.0, 1263.0, 1263.0],
    }

    result = pl.read_json(json.dumps(response).encode())

    assert result.to_dict(as_series=False) == {k: [v] for k, v in response.items()}


def test_ndjson_ignore_errors() -> None:
    # this schema is inconsistent as "value" is string and object
    jsonl = r"""{"Type":"insert","Key":[1],"SeqNo":1,"Timestamp":1,"Fields":[{"Name":"added_id","Value":2},{"Name":"body","Value":{"a": 1}}]}
    {"Type":"insert","Key":[1],"SeqNo":1,"Timestamp":1,"Fields":[{"Name":"added_id","Value":2},{"Name":"body","Value":{"a": 1}}]}"""

    buf = io.BytesIO(jsonl.encode())

    # check if we can replace with nulls
    assert pl.read_ndjson(buf, ignore_errors=True).to_dict(as_series=False) == {
        "Type": ["insert", "insert"],
        "Key": [[1], [1]],
        "SeqNo": [1, 1],
        "Timestamp": [1, 1],
        "Fields": [
            [{"Name": "added_id", "Value": "2"}, {"Name": "body", "Value": '{"a": 1}'}],
            [{"Name": "added_id", "Value": "2"}, {"Name": "body", "Value": '{"a": 1}'}],
        ],
    }

    schema = {
        "Fields": pl.List(
            pl.Struct([pl.Field("Name", pl.String), pl.Field("Value", pl.Int64)])
        )
    }
    # schema argument only parses Fields
    assert pl.read_ndjson(buf, schema=schema, ignore_errors=True).to_dict(
        as_series=False
    ) == {
        "Fields": [
            [{"Name": "added_id", "Value": 2}, {"Name": "body", "Value": None}],
            [{"Name": "added_id", "Value": 2}, {"Name": "body", "Value": None}],
        ]
    }

    # schema_overrides argument does schema inference, but overrides Fields
    result = pl.read_ndjson(buf, schema_overrides=schema, ignore_errors=True)
    expected = {
        "Type": ["insert", "insert"],
        "Key": [[1], [1]],
        "SeqNo": [1, 1],
        "Timestamp": [1, 1],
        "Fields": [
            [{"Name": "added_id", "Value": 2}, {"Name": "body", "Value": None}],
            [{"Name": "added_id", "Value": 2}, {"Name": "body", "Value": None}],
        ],
    }
    assert result.to_dict(as_series=False) == expected


def test_json_null_infer() -> None:
    json = BytesIO(
        bytes(
            """
    [
      {
        "a": 1,
        "b": null
      }
    ]
    """,
            "UTF-8",
        )
    )

    assert pl.read_json(json).schema == OrderedDict({"a": pl.Int64, "b": pl.Null})


def test_ndjson_null_buffer() -> None:
    data = io.BytesIO(
        b"""\
    {"id": 1, "zero_column": 0, "empty_array_column": [], "empty_object_column": {}, "null_column": null}
    {"id": 2, "zero_column": 0, "empty_array_column": [], "empty_object_column": {}, "null_column": null}
    {"id": 3, "zero_column": 0, "empty_array_column": [], "empty_object_column": {}, "null_column": null}
    {"id": 4, "zero_column": 0, "empty_array_column": [], "empty_object_column": {}, "null_column": null}
    """
    )

    assert pl.read_ndjson(data).schema == OrderedDict(
        [
            ("id", pl.Int64),
            ("zero_column", pl.Int64),
            ("empty_array_column", pl.List(pl.Null)),
            ("empty_object_column", pl.Struct([])),
            ("null_column", pl.Null),
        ]
    )


def test_ndjson_null_inference_13183() -> None:
    assert pl.read_ndjson(
        b"""
    {"map": "a", "start_time": 0.795, "end_time": 1.495}
    {"map": "a", "start_time": 1.6239999999999999, "end_time": 2.0540000000000003}
    {"map": "c", "start_time": 2.184, "end_time": 2.645}
    {"map": "a", "start_time": null, "end_time": null}
    """.strip()
    ).to_dict(as_series=False) == {
        "map": ["a", "a", "c", "a"],
        "start_time": [0.795, 1.6239999999999999, 2.184, None],
        "end_time": [1.495, 2.0540000000000003, 2.645, None],
    }


def test_ndjson_expected_null_got_object_inference_22807() -> None:
    buf = io.StringIO()
    for _ in range(100):
        buf.write('{"a":[]}\n')
    buf.write('{"a":[{"b":[]}]}\n')

    buf.seek(0)

    assert pl.read_ndjson(buf, infer_schema_length=None).schema == (
        {"a": pl.List(pl.Struct([pl.Field("b", pl.List(pl.Null))]))}
    )


@pytest.mark.may_fail_cloud  # reason: object
@pytest.mark.write_disk
def test_json_wrong_input_handle_textio(tmp_path: Path) -> None:
    # This shouldn't be passed, but still we test if we can handle it gracefully
    df = pl.DataFrame(
        {
            "x": [1, 2, 3],
            "y": ["a", "b", "c"],
        }
    )
    file_path = tmp_path / "test.ndjson"
    df.write_ndjson(file_path)

    with file_path.open() as f:
        result = pl.read_ndjson(f)
        assert_frame_equal(result, df)


def test_json_normalize() -> None:
    data = [
        {"id": 1, "name": {"first": "Coleen", "last": "Volk"}},
        {"name": {"given": "Mark", "family": "Regner"}},
        {"id": 2, "name": "Faye Raker"},
    ]

    assert pl.json_normalize([], schema=pl.Schema({"test": pl.Int32})).to_dict(
        as_series=False
    ) == {
        "test": [],
    }

    assert pl.json_normalize(data, max_level=0).to_dict(as_series=False) == {
        "id": [1, None, 2],
        "name": [
            '{"first": "Coleen", "last": "Volk"}',
            '{"given": "Mark", "family": "Regner"}',
            "Faye Raker",
        ],
    }

    assert pl.json_normalize(data, max_level=1).to_dict(as_series=False) == {
        "id": [1, None, 2],
        "name.first": ["Coleen", None, None],
        "name.last": ["Volk", None, None],
        "name.given": [None, "Mark", None],
        "name.family": [None, "Regner", None],
        "name": [None, None, "Faye Raker"],
    }

    data = [
        {
            "id": 1,
            "name": "Cole Volk",
            "fitness": {"height": 130, "weight": 60},
        },
        {"name": "Mark Reg", "fitness": {"height": 130, "weight": 60}},
        {
            "id": 2,
            "name": "Faye Raker",
            "fitness": {"height": 130, "weight": 60},
        },
    ]
    assert pl.json_normalize(data, max_level=1, separator=":").to_dict(
        as_series=False,
    ) == {
        "id": [1, None, 2],
        "name": ["Cole Volk", "Mark Reg", "Faye Raker"],
        "fitness:height": [130, 130, 130],
        "fitness:weight": [60, 60, 60],
    }
    assert pl.json_normalize(data, max_level=0).to_dict(
        as_series=False,
    ) == {
        "id": [1, None, 2],
        "name": ["Cole Volk", "Mark Reg", "Faye Raker"],
        "fitness": [
            '{"height": 130, "weight": 60}',
            '{"height": 130, "weight": 60}',
            '{"height": 130, "weight": 60}',
        ],
    }
    assert pl.json_normalize(data, max_level=0, encoder=orjson.dumps).to_dict(
        as_series=False,
    ) == {
        "id": [1, None, 2],
        "name": ["Cole Volk", "Mark Reg", "Faye Raker"],
        "fitness": [
            b'{"height":130,"weight":60}',
            b'{"height":130,"weight":60}',
            b'{"height":130,"weight":60}',
        ],
    }


def test_empty_json() -> None:
    df = pl.read_json(io.StringIO("{}"))
    assert df.shape == (0, 0)
    assert isinstance(df, pl.DataFrame)

    df = pl.read_json(b'{"j":{}}')
    assert df.dtypes == [pl.Struct([])]
    assert df.shape == (1, 1)


def test_compressed_json() -> None:
    # shared setup
    json_obj = [
        {"id": 1, "name": "Alice", "trusted": True},
        {"id": 2, "name": "Bob", "trusted": True},
        {"id": 3, "name": "Carol", "trusted": False},
    ]
    expected = pl.DataFrame(json_obj, orient="row")
    json_bytes = json.dumps(json_obj).encode()

    # gzip
    compressed_bytes = gzip.compress(json_bytes)
    out = pl.read_json(compressed_bytes)
    assert_frame_equal(out, expected)

    # zlib
    compressed_bytes = zlib.compress(json_bytes)
    out = pl.read_json(compressed_bytes)
    assert_frame_equal(out, expected)

    # zstd
    compressed_bytes = zstandard.compress(json_bytes)
    out = pl.read_json(compressed_bytes)
    assert_frame_equal(out, expected)

    # no compression
    uncompressed = io.BytesIO(json_bytes)
    out = pl.read_json(uncompressed)
    assert_frame_equal(out, expected)


def test_empty_list_json() -> None:
    df = pl.read_json(io.StringIO("[]"))  #
    assert df.shape == (0, 0)
    assert isinstance(df, pl.DataFrame)

    df = pl.read_json(b"[]")
    assert df.shape == (0, 0)
    assert isinstance(df, pl.DataFrame)


def test_json_infer_3_dtypes() -> None:
    # would SO before
    df = pl.DataFrame({"a": ["{}", "1", "[1, 2]"]})

    with pytest.raises(pl.exceptions.ComputeError):
        df.select(pl.col("a").str.json_decode(pl.Int64))

    df = pl.DataFrame({"a": [None, "1", "[1, 2]"]})
    out = df.select(pl.col("a").str.json_decode(dtype=pl.List(pl.String)))
    assert out["a"].to_list() == [None, ["1"], ["1", "2"]]
    assert out.dtypes[0] == pl.List(pl.String)


# NOTE: This doesn't work for 0, but that is normal
@pytest.mark.parametrize("size", [1, 2, 13])
def test_zfs_json_roundtrip(size: int) -> None:
    a = pl.Series("a", [{}] * size, pl.Struct([])).to_frame()

    f = io.StringIO()
    a.write_json(f)

    f.seek(0)
    assert_frame_equal(a, pl.read_json(f))


def test_read_json_raise_on_data_type_mismatch() -> None:
    with pytest.raises(ComputeError):
        pl.read_json(
            b"""\
[
    {"a": null},
    {"a": 1}
]
""",
            infer_schema_length=1,
        )


def test_read_json_struct_schema() -> None:
    with pytest.raises(ComputeError, match="extra field in struct data: b"):
        pl.read_json(
            b"""\
[
    {"a": 1},
    {"a": 2, "b": 2}
]
""",
            infer_schema_length=1,
        )

    assert_frame_equal(
        pl.read_json(
            b"""\
[
    {"a": 1},
    {"a": 2, "b": 2}
]
""",
            infer_schema_length=2,
        ),
        pl.DataFrame({"a": [1, 2], "b": [None, 2]}),
    )

    # If the schema was explicitly given, then we ignore extra fields.
    # TODO: There should be a `columns=` parameter to this.
    assert_frame_equal(
        pl.read_json(
            b"""\
[
    {"a": 1},
    {"a": 2, "b": 2}
]
""",
            schema={"a": pl.Int64},
        ),
        pl.DataFrame({"a": [1, 2]}),
    )


def test_read_ndjson_inner_list_types_18244() -> None:
    assert pl.read_ndjson(
        io.StringIO("""{"a":null,"b":null,"c":null}"""),
        schema={
            "a": pl.List(pl.String),
            "b": pl.List(pl.Int32),
            "c": pl.List(pl.Float64),
        },
    ).schema == (
        {"a": pl.List(pl.String), "b": pl.List(pl.Int32), "c": pl.List(pl.Float64)}
    )


def test_read_json_utf_8_sig_encoding() -> None:
    data = [{"a": [1, 2], "b": [1, 2]}]
    result = pl.read_json(json.dumps(data).encode("utf-8-sig"))
    expected = pl.DataFrame(data)
    assert_frame_equal(result, expected)


@pytest.mark.may_fail_cloud  # reason: object
def test_write_masked_out_list_22202() -> None:
    df = pl.DataFrame({"x": [1, 2], "y": [None, 3]})

    output_file = io.BytesIO()

    query = (
        df.group_by("x", maintain_order=True)
        .all()
        .select(pl.when(pl.col("y").list.sum() > 0).then("y"))
    )

    eager = query.write_ndjson().encode()

    query.lazy().sink_ndjson(output_file)
    lazy = output_file.getvalue()

    assert eager == lazy


def test_nested_datetime_ndjson() -> None:
    f = io.StringIO(
        """{"start_date":"2025-03-14T09:30:27Z","steps":[{"id":1,"start_date":"2025-03-14T09:30:27Z"},{"id":2,"start_date":"2025-03-14T09:31:27Z"}]}"""
    )

    schema = {
        "start_date": pl.Datetime,
        "steps": pl.List(pl.Struct({"id": pl.Int64, "start_date": pl.Datetime})),
    }

    assert pl.read_ndjson(f, schema=schema).to_dict(as_series=False) == {  # type: ignore[arg-type]
        "start_date": [datetime(2025, 3, 14, 9, 30, 27)],
        "steps": [
            [
                {"id": 1, "start_date": datetime(2025, 3, 14, 9, 30, 27)},
                {"id": 2, "start_date": datetime(2025, 3, 14, 9, 31, 27)},
            ]
        ],
    }


def test_ndjson_22229() -> None:
    li = [
        '{ "campaign": {  "id": "123456" }, "metrics": { "conversions": 7}}',
        '{ "campaign": {  "id": "654321" }, "metrics": { "conversions": 3.5}}',
    ]

    assert pl.read_ndjson(io.StringIO("\n".join(li))).to_dict(as_series=False)


def test_json_encode_enum_23826() -> None:
    s = pl.Series("a", ["b"], dtype=pl.Enum(["b"]))
    assert_series_equal(
        s.to_frame().select(c=pl.struct("a").struct.json_encode()).to_series(),
        pl.Series("c", ['{"a":"0"}'], pl.String),
    )


@pytest.mark.parametrize(
    "ndjson_str", ["10", "null", "true", "false", "1.5", "[]", "[1, 2]"]
)
def test_ndjson_row_not_an_object_24267(ndjson_str: str) -> None:
    with pytest.raises(
        ComputeError, match="NDJSON line expected to contain JSON object: "
    ):
        pl.read_ndjson(
            io.StringIO(ndjson_str), ignore_errors=False, infer_schema_length=100
        )

    with pytest.raises(
        ComputeError, match="NDJSON line expected to contain JSON object: "
    ):
        pl.read_ndjson(
            io.StringIO('{"a": 10}\n' + ndjson_str),
            ignore_errors=False,
            infer_schema_length=1,
        )

    assert_frame_equal(
        pl.read_ndjson(
            io.StringIO('{"a": 10}\n' + ndjson_str),
            infer_schema_length=1,
            ignore_errors=True,
        ),
        pl.DataFrame({"a": [10, None]}),
    )


def test_ndjson_no_cast_int_to_float_19138() -> None:
    with pytest.raises(
        ComputeError, match=re.escape("cannot parse '2.7' (f64) as Int64")
    ):
        pl.read_ndjson(
            io.StringIO('{"a": 1}\n{"a": 2.7}\n'),
            infer_schema_length=1,
            ignore_errors=False,
        )

    assert_frame_equal(
        pl.read_ndjson(
            io.StringIO('{"a": 1}\n{"a": 2.7}\n'),
            infer_schema_length=1,
            ignore_errors=True,
        ),
        pl.DataFrame({"a": [1, None]}),
    )

    assert_frame_equal(
        pl.read_ndjson(
            io.StringIO('{"a": 2.7}\n{"a": 1}\n'),
            infer_schema_length=1,
            ignore_errors=False,
        ),
        pl.DataFrame({"a": [2.7, 1]}),
    )
